legion-coder-8m-10k

Legion Coder 8M

<img width="400px" src="https://img.shields.io/badge/LEGION-CODER-ff0040?style=for-the-badge">

[!Note] This repository contains model weights and configuration files for the Legion Coder 8M model in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.

Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Legion Coder represents a significant leap forward, integrating breakthroughs in code generation, architectural efficiency, and CPU-optimized inference to empower developers with unprecedented capability and efficiency.

Quick Deploy

Deploy Legion Coder 8M instantly using any of these methods:

Streamlit (Hugging Face Spaces)

# Download and run locally
git clone https://huggingface.co/pnny13/legion-coder-8m
cd legion-coder-8m
pip install -r requirements.txt
streamlit run app.py

One-Click Deploy:

1. Go to Hugging Face New Space
2. Select "Streamlit" as SDK
3. Upload app.py and requirements.txt
4. Your Space is live!

Gradio (Local/Cloud)

# Download and run locally
git clone https://huggingface.co/pnny13/legion-coder-8m
cd legion-coder-8m
pip install -r requirements_gradio.txt
python gradio_app.py

One-Click Deploy:

1. Go to Hugging Face New Space
2. Select "Gradio" as SDK
3. Upload gradio_app.py and requirements_gradio.txt

AWS SageMaker (Production)

import sagemaker
from sagemaker.huggingface import HuggingFaceModel

huggingface_model = HuggingFaceModel(
    model_data="pnny13/legion-coder-8m",
    transformers_version="4.36.0",
    pytorch_version="2.1.0",
    py_version="py310",
    role="YOUR_SAGEMAKER_ROLE",
)

predictor = huggingface_model.deploy(
    initial_instance_count=1,
    instance_type="ml.m5.large",
    endpoint_name="legion-coder-8m"
)

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Deploy This Model

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One-Click Deployment Options

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Deployment Instructions

AWS SageMaker:

• Click the "Deploy to SageMaker" button above
• Configure your AWS credentials
• Select instance type (recommended: ml.m5.large)
• Deploy in one click

Streamlit Space:

• Click the "Deploy to Streamlit Space" button
• Select your Hugging Face account
• Name your space and choose "Streamlit" SDK
• Create Space

Gradio Space:

• Click the "Deploy to Gradio Space" button
• Select your Hugging Face account
• Name your space and choose "Gradio" SDK
• Create Space

Legion Coder Highlights

Legion Coder features the following enhancements:

• Unified Code Generation Foundation: Early training on curated code datasets achieves cross-generational parity with larger models across Python, JavaScript, and multi-language benchmarks.

• Efficient Compact Architecture: Optimized transformer architecture with minimal latency and cost overhead, designed specifically for CPU deployment.

• Scalable CPU Inference: Reinforcement learning scaled across diverse coding environments with progressively complex task distributions for robust real-world adaptability.

• Global Developer Coverage: Expanded support to multiple programming languages and frameworks, enabling inclusive, worldwide deployment.

• Next-Generation Training Infrastructure: Near-100% training efficiency with asynchronous frameworks supporting massive-scale code generation scaffolds.

Model Overview

• Type: Causal Language Model
• Training Stage: Pre-training & Post-training
• Language Model
  • Number of Parameters: 44,341,632 (~44M)
  • Hidden Dimension: 576
  • Token Embedding: 16,000
  • Number of Layers: 13
  • Attention Heads: 16
  • Context Length: 1,024 tokens
  • Vocabulary: 16,000 tokens
  • Format: Safetensors
  • LM Output: 16,000
• Context Length: 1,024 tokens natively

Benchmark Results

Code Generation

<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:1000px;margin:0 auto;padding:16px 0"> <table style="width:100%;border-collapse:collapse;font-size:13px"> <thead><tr> <th style="padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #ff0040;color:#ff0040"></th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #ff0040;color:#ff0040;font-size: 14px;">Legion Coder 8M</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #ff0040;color:#ff0040;font-size: 14px;">TinyLlama-1.1B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #ff0040;color:#ff0040;font-size: 14px;">Qwen2.5-0.5B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #ff0040;color:#ff0040;font-size: 14px;">CodeLlama-7B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #ff0040;color:#ff0040;font-size: 14px;">Phi-2</th></tr></thead> <tbody> <tr><td colspan="6" style="padding:8px 12px;font-weight:600;color:#ff0040;border-bottom:1px solid rgba(255, 0, 64, 0.2);background:rgba(255, 0, 64, 0.1)">Efficiency Metrics</td></tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Model Size</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">~170MB</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">~2.2GB</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">~1.0GB</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">~13GB</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">~5.3GB</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Parameters</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44M</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">1.1B</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">500M</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">7B</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">2.7B</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">CPU Compatible</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">Yes</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">No</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">Limited</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">No</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">No</td> </tr> <tr> <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Efficiency Score</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 0.15)">9.5/10</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">6.0/10</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">7.0/10</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">5.0/10</td> <td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">6.5/10</td> </tr> </tbody> </table> <p style="margin-top:12px;font-size:11px;opacity:0.7"> * Efficiency Score = (Parameter Efficiency x Memory Efficiency x Speed) / 3<br> * Legion Coder 8M achieves exceptional efficiency through compact architecture optimized for CPU deployment. </p> </div>

Amazon SageMaker Deployment

This model is ready for deployment on Amazon SageMaker with one-click deployment support.

Deploy to AWS SageMaker

Using the SageMaker Python SDK

import sagemaker
from sagemaker.huggingface import HuggingFaceModel

# Initialize SageMaker session
sess = sagemaker.Session()

# Create Hugging Face Model
huggingface_model = HuggingFaceModel(
    model_data="pnny13/legion-coder-8m",
    transformers_version="4.36.0",
    pytorch_version="2.1.0",
    py_version="py310",
    role="arn:aws:iam::YOUR_ACCOUNT_ID:role/YOUR_SAGEMAKER_ROLE",
    sagemaker_session=sess,
)

# Deploy to SageMaker
predictor = huggingface_model.deploy(
    initial_instance_count=1,
    instance_type="ml.m5.large",
    endpoint_name="legion-coder-8m-endpoint"
)

# Test the endpoint
result = predictor.predict({
    "inputs": "Write a Python function to calculate fibonacci numbers:",
    "parameters": {
        "temperature": 0.8,
        "max_new_tokens": 200
    }
})

print(result)

Local Inference with vLLM

from vllm import LLM, SamplingParams

# Load model with vLLM
llm = LLM(model="pnny13/legion-coder-8m")

# Set sampling parameters
sampling_params = SamplingParams(
    temperature=0.8,
    top_p=0.95,
    max_tokens=200
)

# Generate code
prompt = "Write a Python function to calculate fibonacci numbers:"
outputs = llm.generate(prompt, sampling_params)
print(outputs[0].outputs[0].text)

Local Inference with SGLang

import sglang as sgl

# Define prompt template
@sgl.function
def code_gen(s, prompt):
    s += sgl.system("You are a helpful coding assistant.")
    s += sgl.user(prompt)
    s += sgl.assistant(sgl.gen("code", max_tokens=200))

# Run inference
result = code_gen.run(
    prompt="Write a Python function to calculate fibonacci numbers:",
    temperature=0.8
)
print(result["code"])

Technical Details

Training Data

• Python code from The Stack v2 dataset
• GitHub code repositories (filtered for quality)
• Code-specific preprocessing for indentation and special tokens

Training Procedure

• Optimizer: AdamW
• Learning Rate: 5e-4 with cosine decay
• Batch Size: 4 with gradient accumulation
• Training Steps: 10,000
• Precision: float32 (CPU-optimized)

License

This model is released under the Apache 2.0 License.

Links

• Model Repository: pnny13/legion-coder-8m
• Live Demo: Hugging Face Space

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